Prediction of the natural gas consumption in chemical processing facilities with genetic programming

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@Article{Kovacic:2016:GPEM,

• author = "Miha Kovacic and Franjo Dolenc",
• title = "Prediction of the natural gas consumption in chemical processing facilities with genetic programming",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2016",
• volume = "17",
• number = "3",
• pages = "231--249",
• keywords = "genetic algorithms, genetic programming, Natural gas consumption prediction, Chemical processing, Modelling",
• ISSN = "1389-2576",
• URL = "http://link.springer.com/article/10.1007/s10710-016-9264-x?wt_mc=internal.event.1.SEM.ArticleAuthorOnlineFirst",
• DOI = "doi:10.1007/s10710-016-9264-x",
• size = "19 pages",
• abstract = "Cinkarna Ltd. is a chemical processing company in Slovenia and the country's largest manufacturer of titanium oxides (TiO2). Chemical processing and titanium oxide manufacturing in particular requires high natural gas consumption, and it is difficult to accurately pre-order gas from suppliers. In accordance with the Energy Agency of the Republic of Slovenia regulations, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences total 1.11 percent of supplied natural gas costs (average 50960 EUR per year). This paper presents natural gas consumption prediction and the minimization of associated costs. The data on daily temperature, steam boilers, sulphuric acid and TiO2 production was collected from January 2012 until November 2014. Based on the collected data, a linear regression and a genetic programming model were developed. Compared to the specialist's prediction of natural gas consumption, the linear regression and genetic programming models reduce the charges for the differences between the ordered and the actually supplied quantities by 3.00 and 5.30 times, respectively. Also, from January until November 2014 the same genetic programming model was used in practice. The results show that in a similar gas consumption regime the differences between the ordered and the actually supplied quantities are statistically significant, namely, they are 3.19 times lower (t test, p < 0.05) than in the period in which the specialist responsible for natural gas consumption made the predictions.",

}

Genetic Programming entries for Miha Kovacic Franjo Dolenc

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